About This PhD Project

Project Description

This project will develop code for GPU-accelerated simulation of the high-dimensional probability density functions found in turbulent reacting flows. In contrast to established adaptive meshing approaches that work in three dimensions, this project will involve creating new adaptive mesh procedures and codes that will be applicable to an arbitrary and time-varying number of dimensions.

Applicants should have a first-class degree in a relevant discipline of engineering, physics or mathematics, experience in programming, strong interest to work on state-of-the art simulation and to apply this to real-world problems. Furthermore, skills supporting communication across discipline boundaries are desired.

In chemically-reacting flows found in nature and engineering, and particularly in combustion systems, turbulent mixing leads to statistical fluctuations. Not only does turbulent flow cause the chemical composition to fluctuate in space and in time, the properties of fuel sprays or soot particles carried in the flow also exhibit fluctuations. Direct numerical simulation of a turbulent reacting flow, resolving features at scales spanning many orders of magnitude – from the nano-scale of soot particles up to the scale of practical combustion systems – is not computationally feasible, even considering the projected growth in supercomputing capacity. Therefore a statistical description based on probability density functions is needed in order to describe the distribution of the numerous different chemical, spray and particle properties that are important to the description of the processes within the flow.

The techniques that we propose can be applied to simulation and control in a wide range of situations – from finance to space debris – but we will focus on modelling of sooting combustion because this exhibits elaborate multi-dimensional behaviour, is tractable with the proposed methods, and is one of the biggest problems currently facing automotive and aerospace engine manufacturers.

This project is run through participation in the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling (http://ngcm.soton.ac.uk). For details of our 4 Year PhD programme, please see http://www.findaphd.com/search/PhDDetails.aspx?CAID=331&LID=2652

For a details of available projects click here http://www.ngcm.soton.ac.uk/projects/index.html

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